NETs are extracellular defense mechanisms used by neutrophils, where chromatin is expelled together with histones and granular/cytoplasmic proteins.

Although NETs play a role in trapping and killing extracellular pathogens, thereby preventing dissemination, NETs can also be injurious and are considered to play a role in a wide array of inflammatory diseases.

Identity of NETS Was Only Discovered in 2004

The identity of NETs was only discovered in 2004 and researchers still have much to learn about them.

“People are trying to quantify this structure. Does the presence of traps mean something?” says Sarder, senior author on the paper. “It’s a complicated process.”

A neutrophil will mix some of its DNA with the toxic granule proteins — usually used to break down molecules or destroy microbes — that it contains in the cell body.

Next, it will eject this DNA and anti-microbial proteins outside of its own cell body. This is called a neutrophil extracellular “trap” because it forms a web of components that can trap microbes from disseminating and can also help break them down. In the process, however, NETs are also dangerous to healthy host tissue, so the use of them must be limited.

Algorithms Created to Detect NETs

“Because of this ‘double-edged sword’ effect, NETs are of high interest in certain diseases, such as cancer and sepsis, or autoimmune diseases such as lupus,” says Ginley, first author and a doctoral student in computational cell biology, anatomy and pathology. “Many researchers would like to know if using NET inhibitor drugs (like PAD4) can be used to improve quality of life or treat these diseases.”

Quantifying the amount of NET presence in an image is very time consuming. Creating a computational technique to automatically identify them can rapidly streamline other research by making NETs faster to quantify.

Ginley worked with his mentor Sarder to develop such algorithms.

“Our major findings were that when imaged by a flow-cytometry technique, morphological descriptors (such as the shape, round or oval) can determine which images are NETs and which are only neutrophils, with high accuracy, and it only requires the DNA to be stained,” says Ginley. “Our other major finding was that when you image NETs in a mouse model of pneumonia — aspergillus fumigatus pneumonia, to be specific — then you can detect NETs by detecting the co-existence of several molecules that are present in NETs.”

Specifically, if a region of the image has high levels of DNA, histones — used to modify DNA to express genes, which are very detectable in NETs — and myeloperoxidase — one of the granular proteins in the cell body — then the region with the co-existent levels of these markers can be defined as NET regions, by measuring how much of the object area contains all three markers at high intensity.

“We found that the morphological structures of NETS in this experiment tend to be long and thin, whereas intact neutrophils tend to be morphologically rounded and circular,” Ginley says.

Method Developed for Classification of NETs

An obstacle in research has been the development of a fully automated image analysis software capable of identifying NETS. Toward that end, the researchers developed a computational method capable of NETs classification and quantification by the imaging of neutrophil DNA.

NETS were defined by the morphological presence of extracellular DNA from purified neutrophils.

The objects in both classes are distinct enough that a support vector machine is able to efficiently discriminate the objects with a high degree of certainty.

“This paper is all about quantifying using computational imaging analysis methods,” Sarder says.

Future work will look to expand their image sets to be larger and more diverse for a number of inflammatory diseases that drive NET generation, such as sepsis and vasculitis.

Work on the Project Began in 2015

“I saw this as a two-way opportunity,” Sarder says. “One was starting a new collaboration by getting a great mentor (Segal) who is very successful, and at the same time getting a student (Ginley) who I can transmit the knowledge to.”

Co-authors Segal and Constantin F. Urban, PhD, of Umeå University in Sweden, conducted visual inspection of immunofluorescent images, which is the current gold standard for NET identification.